app.py

import streamlit as st
import yfinance as yf
import requests
import os
from dotenv import load_dotenv
from langchain.agents import Tool, AgentExecutor, LLMSingleActionAgent, AgentOutputParser
from langchain.schema import AgentAction, AgentFinish, HumanMessage
from langchain.prompts import BaseChatPromptTemplate
from langchain.tools import Tool
from langchain_huggingface import HuggingFacePipeline
from langchain import LLMChain
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
from langchain.memory import ConversationBufferWindowMemory
from statsmodels.tsa.arima.model import ARIMA
import torch
import re
from typing import List, Union
from datetime import datetime
from lumibot.brokers import Alpaca
from lumibot.backtesting import YahooDataBacktesting
from lumibot.strategies.strategy import Strategy
from alpaca_trade_api import REST
from timedelta import Timedelta
from finbert_utils import estimate_sentiment

# Load environment variables from .env
load_dotenv()

NEWSAPI_KEY = os.getenv("NEWSAPI_KEY")
access_token = os.getenv("API_KEY")

# Check if the access token and API key are present
if not NEWSAPI_KEY or not access_token:
    raise ValueError("NEWSAPI_KEY or API_KEY not found in .env file.")

# Alpaca credentials
API_KEY = "PKWJW14IWRJMLJ4CSZ6V"
API_SECRET = "zJOGwUvhYBfYJQRz6jc309PLNfTQ4VcxuygFxxfh"
BASE_URL = "https://paper-api.alpaca.markets/v2"

ALPACA_CREDS = {
    "API_KEY": API_KEY,
    "API_SECRET": API_SECRET,
    "PAPER": True
}

# Initialize the model and tokenizer for the HuggingFace pipeline
tokenizer = AutoTokenizer.from_pretrained("google/gemma-2b-it", token=access_token)
model = AutoModelForCausalLM.from_pretrained(
    "google/gemma-2b-it",
    torch_dtype=torch.bfloat16,
    token=access_token
)
pipe = pipeline("text-generation", model=model, tokenizer=tokenizer, max_new_tokens=512)

# Define functions for fetching stock data, news, and moving averages
def validate_ticker(ticker):
    return ticker.strip().upper()

def fetch_stock_data(ticker):
    try:
        ticker = ticker.strip().upper()
        stock = yf.Ticker(ticker)
        hist = stock.history(period="1mo")
        if hist.empty:
            return {"error": f"No data found for ticker {ticker}"}
        return hist.tail(5).to_dict()
    except Exception as e:
        return {"error": str(e)}

def fetch_stock_news(ticker, NEWSAPI_KEY):
    api_url = f"https://newsapi.org/v2/everything?q={ticker}&apiKey={NEWSAPI_KEY}"
    response = requests.get(api_url)
    if response.status_code == 200:
        articles = response.json().get('articles', [])
        return [{"title": article['title'], "description": article['description']} for article in articles[:5]]
    else:
        return [{"error": "Unable to fetch news."}]

def calculate_moving_average(ticker, window=5):
    stock = yf.Ticker(ticker)
    hist = stock.history(period="1mo")
    hist[f"{window}-day MA"] = hist["Close"].rolling(window=window).mean()
    return hist[["Close", f"{window}-day MA"]].tail(5)

def analyze_sentiment(news_articles):
    sentiment_pipeline = pipeline("sentiment-analysis")
    results = [{"title": article["title"],
                "sentiment": sentiment_pipeline(article["description"] or article["title"])[0]}
               for article in news_articles]
    return results

def predict_stock_price(ticker, days=5):
    stock = yf.Ticker(ticker)
    hist = stock.history(period="6mo")
    if hist.empty:
        return {"error": f"No data found for ticker {ticker}"}

    model = ARIMA(hist["Close"], order=(5, 1, 0))
    model_fit = model.fit()
    forecast = model_fit.forecast(steps=days)
    return forecast.tolist()

def compare_stocks(ticker1, ticker2):
    data1 = fetch_stock_data(ticker1)
    data2 = fetch_stock_data(ticker2)
    if "error" in data1 or "error" in data2:
        return {"error": "Could not fetch stock data for comparison."}
    comparison = {
        ticker1: {"recent_close": data1["Close"][-1]},
        ticker2: {"recent_close": data2["Close"][-1]},
    }
    return comparison

def execute_alpaca_trading():
    class MLTrader(Strategy):
        def initialize(self, symbol: str = "SPY", cash_at_risk: float = .5):
            self.symbol = symbol
            self.sleeptime = "24H"
            self.last_trade = None
            self.cash_at_risk = cash_at_risk
            self.api = REST(base_url=BASE_URL, key_id=API_KEY, secret_key=API_SECRET)

        def position_sizing(self):
            cash = self.get_cash()
            last_price = self.get_last_price(self.symbol)
            quantity = round(cash * self.cash_at_risk / last_price, 0)
            return cash, last_price, quantity

        def get_dates(self):
            today = self.get_datetime()
            three_days_prior = today - Timedelta(days=3)
            return today.strftime('%Y-%m-%d'), three_days_prior.strftime('%Y-%m-%d')

        def get_sentiment(self):
            today, three_days_prior = self.get_dates()
            news = self.api.get_news(symbol=self.symbol, 
                                     start=three_days_prior, 
                                     end=today)
            news = [ev.__dict__["_raw"]["headline"] for ev in news]
            probability, sentiment = estimate_sentiment(news)
            return probability, sentiment

        def on_trading_iteration(self):
            cash, last_price, quantity = self.position_sizing()
            probability, sentiment = self.get_sentiment()

            if cash > last_price:
                if sentiment == "positive" and probability > .999:
                    if self.last_trade == "sell":
                        self.sell_all()
                    order = self.create_order(
                        self.symbol,
                        quantity,
                        "buy",
                        type="bracket",
                        take_profit_price=last_price * 1.20,
                        stop_loss_price=last_price * .95
                    )
                    self.submit_order(order)
                    self.last_trade = "buy"
                elif sentiment == "negative" and probability > .999:
                    if self.last_trade == "buy":
                        self.sell_all()
                    order = self.create_order(
                        self.symbol,
                        quantity,
                        "sell",
                        type="bracket",
                        take_profit_price=last_price * .8,
                        stop_loss_price=last_price * 1.05
                    )
                    self.submit_order(order)
                    self.last_trade = "sell"

    start_date = datetime(2021, 1, 1)
    end_date = datetime(2024, 10, 1)
    broker = Alpaca(ALPACA_CREDS)
    strategy = MLTrader(name='mlstrat', broker=broker, 
                        parameters={"symbol": "SPY", 
                                    "cash_at_risk": .5})
    strategy.backtest(
        YahooDataBacktesting, 
        start_date, 
        end_date, 
        parameters={"symbol": "SPY", "cash_at_risk": .5}
    )
    return "Alpaca trading strategy executed and backtested."

# Define LangChain tools
stock_data_tool = Tool(
    name="Stock Data Fetcher",
    func=fetch_stock_data,
    description="Fetch recent stock data for a valid stock ticker symbol (e.g., AAPL for Apple)."
)

stock_news_tool = Tool(
    name="Stock News Fetcher",
    func=lambda ticker: fetch_stock_news(ticker, NEWSAPI_KEY),
    description="Fetch recent news articles about a stock ticker."
)

moving_average_tool = Tool(
    name="Moving Average Calculator",
    func=calculate_moving_average,
    description="Calculate the moving average of a stock over a 5-day window."
)

sentiment_tool = Tool(
    name="News Sentiment Analyzer",
    func=lambda ticker: analyze_sentiment(fetch_stock_news(ticker, NEWSAPI_KEY)),
    description="Analyze the sentiment of recent news articles about a stock ticker."
)

stock_prediction_tool = Tool(
    name="Stock Price Predictor",
    func=predict_stock_price,
    description="Predict future stock prices for a given ticker based on historical data."
)

stock_comparator_tool = Tool(
    name="Stock Comparator",
    func=lambda tickers: compare_stocks(*tickers.split(',')),
    description="Compare the recent performance of two stocks given their tickers, e.g., 'AAPL,MSFT'."
)

alpaca_trading_tool = Tool(
    name="Alpaca Trading Executor",
    func=execute_alpaca_trading,
    description="Run a trading strategy using Alpaca API and backtest results."
)

tools = [
    stock_data_tool, 
    stock_news_tool, 
    moving_average_tool, 
    sentiment_tool, 
    stock_prediction_tool, 
    stock_comparator_tool,
    alpaca_trading_tool
]

# Set up a prompt template with history
template_with_history = """You are SearchGPT, a professional search engine who provides informative answers to users. Answer the following questions as best you can. You have access to the following tools:

{tools}

Use the following format:

Question: the input question you must answer
Thought: you should always think about what to do
Action: the action to take, should be one of [{tool_names}]
Action Input: the input to the action
Observation: the result of the action
... (this Thought/Action/Action Input/Observation can repeat N times)
Thought: I now know the final answer
Final Answer: the final answer to the original input question

Begin! Remember to give detailed, informative answers

Previous conversation history:
{history}

New question: {input}
{agent_scratchpad}"""

# Set up the prompt template
class CustomPromptTemplate(BaseChatPromptTemplate):
    template: str
    tools: List[Tool]

    def format_messages(self, **kwargs) -> str:
        intermediate_steps = kwargs.pop("intermediate_steps")
        thoughts = ""
        for action, observation in intermediate_steps:
            thoughts += action.log
            thoughts += f"\nObservation: {observation}\nThought: "

        kwargs["agent_scratchpad"] = thoughts
        kwargs["tools"] = "\n".join([f"{tool.name}: {tool.description}" for tool in self.tools])
        kwargs["tool_names"] = ", ".join([tool.name for tool in self.tools])
        formatted = self.template.format(**kwargs)
        return [HumanMessage(content=formatted)]

prompt_with_history = CustomPromptTemplate(
    template=template_with_history,
    tools=tools,
    input_variables=["input", "intermediate_steps", "history"]
)

# Custom output parser
class CustomOutputParser(AgentOutputParser):
    def parse(self, llm_output: str) -> Union[AgentAction, AgentFinish]:
        if "Final Answer:" in llm_output:
            return AgentFinish(
                return_values={"output": llm_output.split("Final Answer:")[-1].strip()},
                log=llm_output,
            )
        regex = r"Action: (.*?)[\n]*Action Input:[\s]*(.*)"
        match = re.search(regex, llm_output, re.DOTALL)
        if not match:
            raise ValueError(f"Could not parse LLM output: `{llm_output}`")
        action = match.group(1).strip()
        action_input = match.group(2)
        return AgentAction(tool=action, tool_input=action_input.strip(" ").strip('"'), log=llm_output)

output_parser = CustomOutputParser()

# Initialize HuggingFace pipeline
llm = HuggingFacePipeline(pipeline=pipe)

# LLM chain
llm_chain = LLMChain(llm=llm, prompt=prompt_with_history)
tool_names = [tool.name for tool in tools]
agent = LLMSingleActionAgent(
    llm_chain=llm_chain, 
    output_parser=output_parser,
    stop=["\nObservation:"], 
    allowed_tools=tool_names
)

memory = ConversationBufferWindowMemory(k=2)
agent_executor = AgentExecutor.from_agent_and_tools(agent=agent, tools=tools, verbose=True, memory=memory)

# Streamlit app
st.title("Trading Helper Agent")

query = st.text_input("Enter your query:")

if st.button("Submit"):
    if query:
        st.write("Debug: User Query ->", query)
        with st.spinner("Processing..."):
            try:
                # Run the agent and get the response
                response = agent_executor.run(query)  # Correct method is `run()`
                st.success("Response:")
                st.write(response)
            except Exception as e:
                st.error(f"An error occurred: {e}")
                # Log the full LLM